Classifying Categorization Using State Trace Analysis and Hierarchical Bayesian Modeling

人类必须对世界进行分类才能理解它。诸如健康与有毒，有价值与高价，甚至苹果与橙子的分类协调我们的思维并指导我们在世界上的行动。与其在认知中的中心地位相一致，目前关于分类和类别学习的理论种类繁多。这个研究项目的目标是收集这些不同的理论，并使用新的和创新的统计理论、计算机实现和实验设计对它们进行直接比较。这个项目旨在通过找出为什么不同的人，在不同的时间，以不同的方式学习不同的类别，来实现苏格拉底的目标，即在其关节处雕刻自然。这个项目有许多潜在的科学和实际影响。特别是，这些有待开发的技术与记忆、判断和决策的心理学研究直接相关。更广泛地说，了解不同类别的学习方式是否可靠地不同，或者人们学习类别的方式是否可靠地不同，将有助于开发程序，帮助人们更快、更准确地学习困难的类别（如癌症x光片与良性x光片）。

Humans must categorize the world in order to understand it. Categories like healthy versus poisonous, valuable versus overpriced, or even apples versus oranges coordinate our thinking and guide our actions in the world. In line with their centrality in cognition, current theories of categorization and category learning are numerous and diverse. The goal of this research project is to gather together these diverse theories and subject them to direct comparison using new and innovative statistical theories, computer implementations, and experimental designs. This project aims to serve Socrate's goal of carving nature at its joints by finding out why different people, at different times, learn different kinds of categories in different ways. This project has many potential scientific and practical ramifications. In particular, the techniques to be developed are directly relevant to research in the psychology of memory, judgment, and decision-making. More broadly, knowing if different category types are reliably different in the way they are learned, or if people are reliably different in the way they learn categories, will allow development of programs to help people learn difficult categories (like cancerous vs benign radiographs) more quickly and accurately.